code: def meta_deform_conv2d(
      input,
      weight,
      offset,
      mask,
      bias,
      stride_h,
      stride_w,
      pad_h,
      pad_w,
      dil_h,
      dil_w,
      n_weight_grps,
      n_offset_grps,
      use_mask,
  ):
      out_height, out_width = offset.shape[-2:]
      out_channels = weight.shape[0]
      batch_size = input.shape[0]
      return input.new_empty(
          (batch_size, out_channels, out_height, out_width)
      )
  
file_path: vision/torchvision/_meta_registrations.py
imports: import functools
  import torch
  import torch._custom_ops
  import torch.library
  import torchvision.extension
label_desc: Use image node_id 12 for calling a global function with example usage: meta_deform_conv2d(input, weight, offset, mask, bias, stride_h, stride_w, pad_h, pad_w, dil_h, dil_w, n_weight_grps, n_offset_grps, use_mask) and returns: input